Unlearn Receives Draft Qualification Opinion from European Medicines Agency for Using the PROCOVA™ Framework to Implement TwinRCTs™

The three-step PROCOVA™ procedure provides a clear framework for implementing Unlearn’s TwinRCT™ solution to accelerate Phase 2 and Phase 3 clinical trials

SAN FRANCISCO--()--Unlearn®, developer of the first machine-learning technology that creates digital twins of patients in clinical trials to enable smaller, faster studies, today announced that the European Medicines Agency (EMA) recently released a draft qualification opinion providing a regulatory framework for the application of the company’s TwinRCT™ solution to Phase 2 and 3 clinical trials. The three-step PROCOVA™ procedure (patent-pending) is the foundation of Unlearn’s TwinRCT™ solution and describes how to use patient-specific prognostic scores derived from participants’ digital twins to run smaller clinical trials without introducing bias.

“Unlearn has always taken a proactive approach to working with regulators in an effort to develop clear frameworks for how our digital twin approach can support clinical trials,” said Charles Fisher, PhD, founder and CEO of Unlearn. “The EMA’s draft opinion is a critically important milestone on this unique path for proactive regulatory qualification of our solution. Although pre-qualification of methods is not required, this ensures our partners can implement TwinRCTs™ in alignment with rigorous regulatory standards.”

Unlearn works with pharmaceutical and biotechnology companies, as well as academic institutions, to optimize human clinical trials with TwinRCTs™. A TwinRCT™ is a randomized trial that uses historical control data and machine learning to achieve a higher probability of success with a smaller number of patients. Treatment effects for the primary and secondary outcomes can all be estimated with greater precision after correcting for each patient’s prognostic score derived from their digital twin.

“I applaud the EMA for their commitment to creating clear guidance and pathways for cutting edge innovations to be introduced into clinical trials,” said Dr. Ann Taylor, former Chief Medical Officer of AstraZeneca and current Unlearn Board Member. “The Unlearn team has done an incredible job in not only developing a solution that will increase trial efficiency but also in providing the rigorous evidence suitable for supporting regulatory decisions.”

For more information on the EMA draft qualification opinion, please visit Unlearn’s website to download a summary and a step-by-step guide for the practical application of PROCOVA™.

About Unlearn

Unlearn.AI is the only company creating TwinRCTs™, which combine AI, digital twins, and novel statistical methods to enable smaller, more efficient clinical trials. Unlearn’s technology has been published in conference abstracts and scientific journals, including Scientific Reports - Nature and The International Journal of Biostatistics. Unlearn is partnering with the world’s leading pharma companies, including Merck KGaA, Darmstadt, Germany, and continues to have discussions with regulators. The European Medicines Agency (EMA) published a draft qualification opinion for PROCOVA™ — a patent-pending method developed by Unlearn for leveraging historical data and machine learning to reduce sample sizes or increase power in Phase 2 or 3 clinical trials. For more information, please visit https://www.unlearn.ai or follow @UnlearnAI on Twitter, @unlearn-ai on LinkedIn.


Colin Sanford